Model-set adaptation using a fuzzy Kalman filter

Authors:
Zhen Ding;Henry Leung;Keith Chan;Zhiwen Zhu
Affiliations:
Advanced Systems Development, Raytheon Systems Canada Limited 400 Philip Street, Waterloo, Ontario, N2J 4K6, Canada;Department of Electrical and Computer Engineering University of Calgary, 2500 University Drive N.W. Calgary, Alberta, T2N 1N4, Canada;Department of Computing, The Hong Kong Polytechnic University Hung Horm, Kowloon, Hong Kong;Toronto Multimedia Application Center, Nortel Networks Toronto, Ontario, M5G 1W7, Canada
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2001

Citing 4
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Abstract

In this paper, a fuzzy Kalman filter (KF) is proposed to combat the model-set adaptation problem of multiple model estimation. The fuzzy KF is found to be able to more exactly extract dynamic information of target maneuvers. It uses a set of fuzzy rules to adaptively control the process noise covariance of the KF and that makes it more suitable for real radar tracking. The proposed fuzzy Kalman filter is then incorporated into an interacting multiple model (IMM) algorithm, hence, a fuzzy IMM (FIMM) algorithm is obtained. The performance of the FIMM algorithm is compared with that of an adaptive IMM (AIMM) algorithm using real radar data. Simulation result shows that the FIMM algorithm greatly outperforms the AIMM algorithm in terms of both the root mean square prediction error and the number of track loss.